Data Science Salaries

Predicting Data Science Salaries

Anh Nguyen, Amira Bendjama, Hong Doan

1. Introduction and Problem Statement
   

   The field of data science has experienced remarkable growth in recent years, with organizations across diverse industries recognizing the value of data-driven decision making. According to an article by 365 Data Science, the US Bureau of Labor Statistics estimated that the employment rate for data scientists will grow by 36% from 2021 to 2031. This rate is significantly higher than the average growth rate of 5%, indicating substantial growth and demand for data science talent. The surging demand for data science presents both opportunities and challenges for job seekers, particularly recent graduates. One of the significant hurdles they face is the lack of salary transparency in the data science job market. This opacity creates uncertainty regarding compensation and hinders job seekers’ ability to negotiate fair salaries.

   There are significant variations in data science salaries across different industries and locations. For instance, according to Zippia, data scientists working in the finance and technology sectors tend to earn higher salaries compared to those in other industries. Similarly, the geographical location also plays a crucial role in determining salaries. Large cities with higher concentration of tech companies and living costs such as San Francisco and New York offer higher salaries than smaller cities.

   The discrepancies in data science salaries can also be attributed to various factors, including job responsibilities, experience level, educational background, and specific skill sets. A study conducted by Burtch Works, a leading executive recruiting firm, found that data scientists with advanced degrees, such as Ph.D., tend to command higher salaries compared to those with bachelor’s or master’s degrees. Similarly, professionals with expertise in specialized areas, such as machine learning or natural language processing, often earn higher salaries due to the high demand for these skills.

   According to a report surveyed 1,000 US-based full-time employees, conducted by Visier, 79% of all survey respondents want some form of pay transparency and 32% want total transparency, in which all employee salaries are publicized. However, the 2022 Pay Clarity Survey by WTW found that only 17% of companies are disclosing pay range information in U.S. locations where not required by state or local laws. For the states that have pay transparency laws such as Colorado and New York, there has been a decline in job postings since the law went into effect. Some employers comply with the new laws by expanding the salary ranges, sometimes to ridiculous lengths. These statistics highlight the lack of pay transparency not only in the field of data science, but across multiple job markets. Job seekers often struggle to estimate salaries for data science positions due to the scarcity of reliable information.

   To address this problem, our project aims to develop a predictive model that estimates the salary for data science jobs. By leveraging publicly available data and employing machine learning algorithms, we seek to provide job seekers a better understanding of salary expectations within the data science job market and empower them to negotiate fair and competitive compensation packages.
   

2. Data Sources and Data preparation

• Install packages

install.packages("rpart.plot")

Installing package into ‘C:/Users/mira/AppData/Local/R/win-library/4.3’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.3/rpart.plot_3.1.1.zip'
Content type 'application/zip' length 1034594 bytes (1010 KB)
downloaded 1010 KB

package ‘rpart.plot’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\mira\AppData\Local\Temp\RtmpkPyMFF\downloaded_packages

install.packages("ggplot2")

Error in install.packages : Updating loaded packages

install.packages("e1071")

Error in install.packages : Updating loaded packages

# Install the plotly package
install.packages("plotly")

Installing package into ‘C:/Users/mira/AppData/Local/R/win-library/4.3’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.3/plotly_4.10.1.zip'
Content type 'application/zip' length 3241461 bytes (3.1 MB)
downloaded 3.1 MB

package ‘plotly’ successfully unpacked and MD5 sums checked

Restarting R session...

• Import data

library(ggplot2)
ds_salaries <- read.csv("ds_salaries.csv")

• Data description

summary(ds_salaries)

       X           work_year    experience_level   employment_type     job_title        
 Min.   :  0.0   Min.   :2020   Length:607         Length:607         Length:607        
 1st Qu.:151.5   1st Qu.:2021   Class :character   Class :character   Class :character  
 Median :303.0   Median :2022   Mode  :character   Mode  :character   Mode  :character  
 Mean   :303.0   Mean   :2021                                                           
 3rd Qu.:454.5   3rd Qu.:2022                                                           
 Max.   :606.0   Max.   :2022                                                           
     salary         salary_currency    salary_in_usd    employee_residence
 Min.   :    4000   Length:607         Min.   :  2859   Length:607        
 1st Qu.:   70000   Class :character   1st Qu.: 62726   Class :character  
 Median :  115000   Mode  :character   Median :101570   Mode  :character  
 Mean   :  324000                      Mean   :112298                     
 3rd Qu.:  165000                      3rd Qu.:150000                     
 Max.   :30400000                      Max.   :600000                     
  remote_ratio    company_location   company_size      
 Min.   :  0.00   Length:607         Length:607        
 1st Qu.: 50.00   Class :character   Class :character  
 Median :100.00   Mode  :character   Mode  :character  
 Mean   : 70.92                                        
 3rd Qu.:100.00                                        
 Max.   :100.00                                        

• The first 5 rows

This dataset has 607 rows and 12 columns

We want to focus on “USD” currency so we keep the “salary_in_usd” column and drop “salary_currency” and “salary” column by using subset()

• Check for null values

There are no null values

• Check for duplicate rows

There are 42 duplicate rows

• Remove duplicates

# Remove duplicate rows
df <- ds_salaries[!duplicated(ds_salaries), ]
# check again
repeated_entries_new <- subset(df, duplicated(df))
print(repeated_entries_new)

Salaries groups

Adding new column to split our salaries into three groups Low , High, Medium.The approach is to use Percentiles by Dividing the dataset based on them. Hence, we are classifying salaries below the 25th percentile as “Low”, salaries between the 25th and 75th percentile as “Medium”, and salaries above the 75th percentile as “High”.

# adding new column 
# Calculate the percentiles
percentiles <- quantile(df$salary_in_usd, probs = c(0.25, 0.75))

# Define the thresholds
low_threshold <- percentiles[1]  # 25th percentile
high_threshold <- percentiles[2]  # 75th percentile

# Create a new column based on percentiles
df$salary_classification <- ifelse(df$salary_in_usd < low_threshold, "Low",
                                   ifelse(df$salary_in_usd > high_threshold, "High", "Medium"))

3. Data Exploration and Visualization

Top 10 Jobs in the dataset:

# Get top 10 job titles and their value counts
top10_job_title <- head(sort(table(df$job_title), decreasing = TRUE), 10)

top10_job_title_df <- data.frame(job_title = names(top10_job_title), count = as.numeric(top10_job_title))
top10_job_title_df

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# Load the required packages
library(plotly)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

# Define custom color palette
custom_colors <- c("#FF6361", "#FFA600", "#FFD700", "#FF76BC", "#69D2E7", "#6A0572", "#FF34B3", "#118AB2", "#FFFF99", "#FFC1CC")

# Create bar plot
fig <- plot_ly(data = top10_job_title_df, x = ~reorder(job_title, -count), y = ~count, type = "bar",
               marker = list(color = custom_colors), text = ~count) %>%
  layout(title = "Top 10 Job Titles", xaxis = list(title = "Job Titles"), yaxis = list(title = "Count"),
         font = list(size = 17), template = "plotly_dark")

# Adjust layout settings to avoid label overlap
fig <- fig %>% layout(
  margin = list(b = 150),  # Increase bottom margin to provide space for labels
  xaxis = list(
    tickangle = 45,  # Rotate x-axis tick labels
    automargin = TRUE  # Automatically adjust margins to avoid overlap
  )
)

# Display the plot
fig

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Experience level categories:

Our Dataset has 4 different experience categories: - EN: Entry-level / Junior - MI: Mid-level / Intermediate - SE: Senior-level / Expert - EX: Executive-level / Director

# Create a mapping of category abbreviations to full names
category_names_experience <- c("EN" = "Entry-level",
                    "MI" = "Mid-level",
                    "SE" = "Senior-level",
                    "EX" = "Executive-level")

# Get the sorted experience data
experience <- head(sort(table(df$experience_level), decreasing = TRUE))

# Replace the category names with full forms
names(experience) <- category_names_experience[names(experience)]

# Calculate the percentage for each category
percentages <- round(100 * experience / sum(experience), 2)

# Define a custom color palette
custom_colors <- c("#FFA998", "#FF76BC", "#69D2E7", "#FFA600")

# Create a pie chart with cute appearance
pie(experience, labels = paste(names(experience), "(", percentages, "%)"), col = custom_colors, border = "white", clockwise = TRUE, init.angle = 90)

# Add a legend with cute colors
legend("topright", legend = names(experience), fill = custom_colors, border = "white", cex = 0.8)

# Add a title with a cute font
title("Experience Distribution", font.main = 1)

Compnay size distribution

# Create a mapping of category abbreviations to full names
category_names_company <- c("M" = "Medium",
                    "L" = "Large",
                    "S" = "Small"
                   )


# Get the sorted company size data
company_size <- head(sort(table(df$company_size), decreasing = TRUE))

# Replace the category names with full forms
names(company_size) <- category_names_company[names(company_size)]

# Set the maximum value for the y-axis
max_count <- max(company_size)

# Create a bar plot with adjusted y-axis limits
barplot(company_size, col = custom_colors, main = "Company Size Distribution", xlab = "Company Size", ylab = "Count", ylim = c(0, max_count + 10))

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Salaries Distribution

# Set the scipen option to a high value
options(scipen = 10)

# Create boxplot of salaries
bp <- boxplot(df$salary_in_usd / 1000, 
        col = "skyblue", 
        main = "Boxplot of Salaries",
        ylab = "Salary in Thousands USD",
        notch = TRUE)

Salaries classification Distribution

# Get the sorted salary classification data
salary_classification <- sort(table(df$salary_classification), decreasing = TRUE)


salary_classification_df <- data.frame(salary_classification= names(salary_classification ), count = as.numeric(salary_classification ))

fig <- plot_ly(
  data = salary_classification_df,
  x = ~reorder(salary_classification, -count),
  y = ~count,
  type = "bar",
  marker = list(color = custom_colors),
  text = ~count,
  width = 700,
  height = 400
)

fig <- fig %>% layout(
  title = "Salary Classification Distribution",
  xaxis = list(title = "Salary Classification"),
  yaxis = list(title = "Count"),
  font = list(size = 17),
  template = "ggplot2"
)

fig

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# Create a data frame with counts of experience levels by salary classification
experience_salary <- table(df$experience_level, df$salary_classification)

# Define custom colors for each experience level
custom_colors <- c("#69D2E7", "#FFA600", "#FF6361", "#FFD700")

# Create a data frame for the plot
plot_data <- data.frame(Experience = rownames(experience_salary), 
                        Salary_Classification = colnames(experience_salary), 
                        Count = as.vector(experience_salary))

# Convert Count column to numeric
plot_data$Count <- as.numeric(plot_data$Count)

# Create the bar plot
library(plotly)
fig <- plot_ly(data = plot_data, x = ~Salary_Classification, y = ~Count, 
               color = ~Experience, colors = custom_colors, type = "bar") %>%
  layout(title = "Experience Level by Salary Classification",
         xaxis = list(title = "Salary Classification"),
         yaxis = list(title = "Count"),
         font = list(size = 17),
         template = "plotly_dark")

fig

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4. Modeling

   a. Logestic Regression

   b. Random Forest

   c. Decision Tree

5. Evaluation and Results

   a. Linear Regression

   b. Random Forest

   c. Decision Tree

6. Major Challenges and Solutions
   

   • Data is not updated

   • Data is imbalanced

7. Conclusion and Future Work

8. References

   The Data Scientist Job Outlook in 2023 | 365 Data Science

   Which Industry Pays the Highest Data Scientist Salary? How To Make The Most Money As A Data Scientist - Zippia

   Burtch-Works-Study_DS-PAP-2019.pdf (burtchworks.com)

   New Visier Report Reveals 79% of Employees Want Pay Transparency (prnewswire.com)

   More NA organizations plan to disclose pay information - WTW (wtwco.com)

   Study: Pay Transparency Reduces Recruiting Costs (shrm.org)